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43rd AIAA DATC/IEEE Digital Avionics systems Conference, DASC 2024

作者： Evangelista Matilde, Felipe Rodrigo Marques, Johnny Aeronautics Institute of Technology Software and Information Systems Department São José dos Campos Brazil

ISBN: (纸本)9798350349610

Operating an airplane is not an easy task. The pilots must check for weight and balance, communicate with towers, flight crew and passengers, fly the airplane, navigate from origin to destination, sometimes deal with system failures, and perform many procedures. Electronic Flight Bags (EFB) are applications pilots use inside the airplane cabin to make operating an airplane easier. This type of software application has been growing in the last twenty years. Most of these applications developed for tablets help pilots considerably operate airplanes by reducing workloads, optimizing airplane performance, and removing paper manuals from airplanes. In 2021, EUROCAE published the standard ED-273 for developing EFBs and introduced some requirements related to software development assurance, among other relevant points. The EUROCAE ED-273, as expected, made EFB manufacturers concerned about how to follow the new requirements. On the other hand, a 30-year-old software standard, the RTCA DO-178C, establishes considerations for developers, installers, and users when designing embedded equipment using software. Several aircraft manufacturers have well-established RTCA DO-178C software processes. We believe it would be very beneficial for organizations with an RTCA DO-178C compliant process to take advantage of it as a means of compliance for the new ED-273. Our work proposes to create an EFB development approach compliant with ED-273 guidelines but using the original RTCA DO-178C processes defined by the aircraft manufacturer. A mapping between the two standards was performed. The development approach was thoroughly assessed by a panel of highly experienced experts in aeronautical software. The main contributions of this paper are (i) the mapping between EUROCAE ED-273 and RTCA DO-178C, (ii) the development approach, and (iii) the evaluation processes defined and executed with the participation of 5 experts in RTCA DO-178C or EFB software development. © 2024 IEEE.

关键词： Application programs

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Data Owner Benefit-Driven Design of People Analytics

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Proceedings of the ACM on Human-Computer Interaction 2023年第EICS期7卷 1-38页

作者： Zander, Patrik Zieglmeier, Valentin Technical University of Munich TUMSchool of Computation Information and Technology Chair of Software and Systems Engineering Boltzmannstr. 3 Garching85748 Germany

With increasingly digitalized workplaces, the potential for sophisticated analyses of employee data rises. This increases the relevance of people analytics (PA), which are tools for the behavioral analysis of employees. Despite this potential, the successful usage of PA is hindered by employee concerns. Especially in Europe, where the GDPR or equivalent laws apply, employee consent is required before data can be processed in PA. Therefore, PA can only provide relevant insights if employees are willing to share their data. One potential way of achieving this is the use of appeal strategies. In the design of PA, the core strategy that can be used is the inclusion of data owner benefits, such as automated feedback, that are given to employees in exchange for sharing their own data. In this paper, we examine benefits as an appeal strategy and develop four design principles for the inclusion of benefits in PA. Then, we describe an exemplary set of analyses and benefits, demonstrating how our principles may be put into practice. Based on this exemplary implementation, we describe and discuss the results of a user study (n = 46) among employees in the EU and UK. Our study investigates the factors that foster or hinder employees' consent to sharing their data with PA. Then, we introduce our data owner benefits and analyze whether they can positively influence this consent decision. Our introduced data owner benefits were, contrary to our expectations, not suited to motivate our participants to consent to sharing their data. We therefore analyze how participants judge the benefits. Participants generally appreciate having them, confirming the value of including data owner benefits when designing PA. Some of our introduced benefits negatively influenced participants' sharing decision, though, meaning that careful consideration of potential risks is required when conceptualizing them. © 2023 ACM.

关键词： Personnel

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Rethinking People Analytics With Inverse Transparency by Design

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arXiv 2023年第CSCW2期7卷 1-29页

作者： Zieglmeier, Valentin Pretschner, Alexander Technical University of Munich TUM School of Computation Information and Technology Software and Systems Engineering Boltzmannstr. 3 Garching85748 Germany

Employees work in increasingly digital environments that enable advanced analytics. Yet, they lack oversight over the systems that process their data. That means that potential analysis errors or hidden biases are hard to uncover. Recent data protection legislation tries to tackle these issues, but it is inadequate. It does not prevent data misusage while at the same time stifling sensible use cases for data. We think the conflict between data protection and increasingly data-driven systems should be solved differently. When access to an employees’ data is given, all usages should be made transparent to them, according to the concept of inverse transparency. This allows individuals to benefit from sensible data usage while addressing the potentially harmful consequences of data misusage. To accomplish this, we propose a new design approach for workforce analytics software we refer to as inverse transparency by design. To understand the developer and user perspectives on the proposal, we conduct two exploratory studies with students. First, we let small teams of developers implement analytics tools with inverse transparency by design to uncover how they judge the approach and how it materializes in their developed tools. We find that architectural changes are made without inhibiting core functionality. The developers consider our approach valuable and technically feasible. Second, we conduct a user study over three months to let participants experience the provided inverse transparency and reflect on their experience. The study models a software development workplace where most work processes are already digital. Participants perceive the transparency as beneficial and feel empowered by it. They unanimously agree that it would be an improvement for the workplace. We conclude that inverse transparency by design is a promising approach to realize accepted and responsible people analytics. Copyright © 2023, The Authors. All rights reserved.

关键词： Transparency

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Improved YOLOv8 for Complex Environmental Fish Detection 7

Improved YOLOv8 for Complex Environmental Fish Detection

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7th IEEE Advanced information technology, Electronic and Automation Control Conference, IAEAC 2024

作者： Cheng, Tonglin Li, Juan Luo, Jintao Li, Zhihui Beijing University of Technology Faculty of Information Technology China Beijing Engineering Research Center for LoT Software and Systems China

ISBN: (纸本)9798350339161

With the rapid development of computer vision and deep learning technologies, object detection plays a crucial role in various fields. Due to the complexity and diversity of underwater environments, underwater vision tasks face great challenges. In this paper, we improved the YOLOv8 detection model for the fish detection task in complex environments. Specifically, we proposed a multi-scale feature fusion method (MFANet) that combines multiscale weighted feature fusion with multiscale attention to efficiently capture multilevel features of fishes in complex environments. We designed a multiscale weighted feature fusion module that combines feature maps at different scales to enhance the model's representation of the target. By introducing a multi-scale attention mechanism, the model is able to pay more attention to important feature regions. We conducted experiments on real underwater complex environment datasets. The results show that our proposed improved method significantly enhances the performance of the YOLOv8 model in fish detection tasks and lays a good foundation for subsequent underwater detection tasks. © 2024 IEEE.

关键词： Fish

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MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Experts 41

MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Exp...

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41st International Conference on Machine Learning, ICML 2024

作者： Zhou, Jianan Cao, Zhiguang Wu, Yaoxin Song, Wen Ma, Yining Zhang, Jie Xu, Chi College of Computing and Data Science Nanyang Technological University Singapore School of Computing and Information Systems Singapore Management University Singapore Department of Information Systems Eindhoven University of Technology Netherlands Institute of Marine Science and Technology Shandong University China Singapore

Learning to solve vehicle routing problems (VRPs) has garnered much ***, most neural solvers are only structured and trained independently on a specific problem, making them less generic and *** this paper, we aim to develop a unified neural solver that can cope with a range of VRP variants ***, we propose a multi-task vehicle routing solver with mixture-of-experts (MVMoE), which greatly enhances the model capacity without a proportional increase in *** further develop a hierarchical gating mechanism for the MVMoE, delivering a good trade-off between empirical performance and computational ***, our method significantly promotes zero-shot generalization performance on 10 unseen VRP variants, and showcases decent results on the few-shot setting and real-world benchmark *** further conduct extensive studies on the effect of MoE configurations in solving VRPs, and observe the superiority of hierarchical gating when facing out-of-distribution *** source code is available at: https://***/RoyalSkye/Routing-MVMoE. Copyright 2024 by the author(s)

关键词： Vehicle routing

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Sentiment analysis in social internet of things using contextual representations and dilated convolution neural network

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Neural computing and Applications 2024年第20期36卷 12357-12370页

作者： Abid, Fazeel Rasheed, Jawad Hamdi, Mohammed Alshahrani, Hani Al Reshan, Mana Saleh Shaikh, Asadullah Department of Information Systems Dr. Hassan Murad School of Management University of Management and Technology Lahore Pakistan Department of Computer Engineering Istanbul Sabahattin Zaim University Istanbul34303 Turkey Department of Software Engineering Istanbul Nisantasi University Istanbul34394 Turkey Department Computer Science College of Computer Science and Information Systems Najran University Najran61441 Saudi Arabia Department Information Systems College of Computer Science and Information Systems Najran University Najran61441 Saudi Arabia

The methodologies based on neural networks are substantial to accomplish sentiment analysis in the Social Internet of Things (SIoT). With social media sentiment analysis, significant insights can produce efficient and intelligent applications. Neural networks such as recurrent neural networks (RNNs) and convolution neural networks (CNNs) have been considered widely in many text classification tasks. However, RNNs are computationally expensive and require complex training to capture contextual information and long-term dependencies. Similarly, traditional CNNs must stack multiple convolutional layers, requiring massive computations and additional parameters. To address these problems, this work initialized the novel architecture, in which contextual representations (CRs) based on the textual framework are proposed at the initial step. In CRs, state-of-the-art word representation models, such as GloVe (global vectors) and FastText (subword information), collectively produce word representations upon the input sequence using a weight mechanism. Secondly, a unique way is introduced: a three-parallel layered dilated convolutional network with global mean pooling. The experimental results show that the proposed methods when compared with baseline methods, the dilation in CNNs following CRs significantly increases the accuracy from 72.45 to 98.98% and reduces computational resources. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Sentiment analysis

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Robust steganographic approach using generative adversarial network and compressive autoencoder

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Multimedia Tools and Applications 2024年 1-38页

作者： Qasaimeh, Malik Qtaish, Alaa Abu Aljawarneh, Shadi Department of Computer Information Systems Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan Department of Software Engineering co-joint with the Department of Cyber Security Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan

Nowadays, social media applications and websites have become a crucial part of people’s lives;for sharing their moments, contacting their families and friends, or even for their jobs. However, the fact that these valuable data are transferred via the Internet and open channels, which are vulnerable to attacks and espionage, requires using defense methods to improve the security of data transmission. Recently, the progress of Deep Learning (DL) inspired researchers to use it with security methods such as steganography, the art of hiding secret data in unrelated content. DL and steganography combined enhanced the hiding properties, especially in the Coverless Image Steganography (CIS) methods. In this paper, we propose a Compressive Coverless Image Steganography (CCIS) model, which is a generated-based CIS, to improve the capacity and robustness of Steganography. This model uses the compressive autoencoder to compress the message, thus increasing the capacity, and uses the Generative Adversarial Network (GAN) to generate a stego image from the compressed vector, thus enhancing concealment ability, besides using a regression model and optimization method to extract the data. Another version was designed to enable sending binary messages by replacing the compressive autoencoder network with a robust mapping rule. Experiments show that the proposed model could improve the characteristics of Steganography. Furthermore, the proposed model could extract binary messages with 256 bits from attacked stego images with 99% recovery accuracy. Thus, capacity and robustness were enhanced using our model with both images and text as secret messages. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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MAPFUNet:Multi-attention Perception-Fusion U-Netfor Liver Tumor Segmentation

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Journal of Bionic Engineering 2024年第5期21卷 2515-2539页

作者： Junding Sun Biao Wang Xiaosheng Wu Chaosheng Tang Shuihua Wang Yudong Zhang School of Computer Science and Technology Henan Polytechnic UniversityJiaozuo451460China Department of Biological Sciences Xi’an Jiaotong-Liverpool UniversitySuzhou215123China School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz University21589JeddahSaudi Arabia

The second-leading cause of cancer-related deaths globally is liver *** treatment of liver cancers depends heavily on the accurate segmentation of liver tumors from CT *** improved method based on U-Net has achieved good perfor-mance for liver tumor segmentation,but these methods can still be *** deal with the problems of poor performance from the original U-Net framework in the segmentation of small-sized liver tumors and the position information of tumors that is seriously lost in the down-sampling process,we propose the Multi-attention Perception-fusion U-Net(MAPFU-Net).We propose the Position ResBlock(PResBlock)in the encoder stage to promote the feature extraction capability of MAPFUNet while retaining the position information regarding liver tumors.A Dual-branch Attention Module(DWAM)is proposed in the skip connections,which narrows the semantic gap between the encoder's and decoder's features and enables the network to utilize the encoder's multi-stage and multi-scale *** propose the Channel-wise ASPP with Atten-tion(CAA)module at the bottleneck,which can be combined with multi-scale features and contributes to the recovery of micro-tumor feature ***,we evaluated MAPFUNet on the LITS2017 dataset and the 3DIRCADB-01 dataset,with Dice values of 85.81 and 83.84%for liver tumor segmentation,which were 2.89 and 7.89%higher than the baseline model,*** experiment results show that MAPFUNet is superior to other networks with better tumor feature representation and higher accuracy of liver tumor *** also extended MAPFUNet to brain tumor segmentation on the BraTS2019 *** results indicate that MAPFUNet performs well on the brain tumor segmentation task,and its Dice values on the three tumor regions are 83.27%(WT),84.77%(TC),and 76.98%(ET),respectively.

关键词： Liver tumor segmentation Small tumors Position information Attention Multi-scale features

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Automated BPMN Diagram Generation 6

Automated BPMN Diagram Generation

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6th International Conference on Advancements in computing, ICAC 2024

作者： Ajmal, Farhath Wijekoon, Poorna Dhanamina, Haritha Ravishan, Yasiru Nawinna, Dasuni Attanayaka, Buddhima Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798331517878

The automation of business process modelling has become crucial for organizations seeking to improve their operational efficiency. This research presents a novel methodology that leverages fine-tuned GPT models to automate the conversion of textual business process descriptions into Business Process Model and Notation (BPMN) diagrams. The study builds on previous work, addressing gaps related to the handling of unstructured text and the absence of anaphora resolution in our solution we use fine-tuned GPT models to accurately extract and structure business process activities from text input from end users, which are then converted into BPMN diagrams using a custom generation framework. The evaluation results demonstrate the system's accuracy with an average precision of 80%, recall of 76%, and F1 score of 78%. These findings outline the potential of using advanced language models to streamline business process modelling, offering significant improvements in accuracy and efficiency. The research concludes by suggesting enhancements for future iterations, including fine-tuning with advanced models and expanding support for complex BPMN elements, aiming to further optimize workflow for organizations. © 2024 IEEE.

关键词： Efficiency

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Deep Learning Techniques for Tuberculosis Detection on Chest X-Rays in Low-Resource Settings: A Survey of Opportunities and Challenges

Deep Learning Techniques for Tuberculosis Detection on Chest...

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2024 International Conference on Electrical and Computer Engineering Researches, ICECER 2024

作者： Sidume, Freedmore Muchuchuti, Stewart Chengetenai, Gibson Tamukate, Rabson Ntwaetsile, Kushata Ntekeng, Patience School of Computing & Information Systems Botswana Accountancy College Gaborone Botswana Botswana International University of Sciences and Technology Computing & Information Systems Palapye Botswana Botswana Teaching Professionals Council Digital Transformation and Innovation Gaborone Botswana

ISBN: (纸本)9798331539733

Tuberculosis (TB) remains a significant public health challenge, particularly in low-resource settings, where limited access to diagnostic tools and healthcare infrastructure exacerbates the burden of the disease. This survey critically examined the application of deep learning techniques for TB detection using chest X-rays, focusing on the opportunities and challenges specific to low-resource environments. This study reviews state-of-the-art deep learning models, such as Convolutional Neural Networks (CNNs), transfer learning frameworks, and hybrid approaches, to assess their effectiveness in enhancing diagnostic accuracy, scalability, and cost-efficiency. The key challenges in implementing these models include the scarcity of annotated data, limited computational resources, and the need for model adaptability across diverse populations. These findings underscore the transformative potential of deep learning in improving TB detection, particularly through automation, integration with telemedicine, and deployment of lightweight, efficient models suitable for resource-constrained settings. This survey highlights the critical need for continued research and innovation to overcome existing barriers and fully realize the benefits of deep learning in TB diagnostics. © 2024 IEEE.

关键词： Deep learning

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